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Image Recognition and Processing Using Artificial Neural Network. Md. Iqbal Quraishi , J Pal Choudhury and Mallika De, IEEE. Outline. Introduction Related work Method Experiments Result and Analysis Conclusion. Introduction.
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Image Recognition and Processing Using Artificial Neural Network Md. IqbalQuraishi, J Pal Choudhury and Mallika De, IEEE
Outline • Introduction • Related work • Method • Experiments Result and Analysis • Conclusion
Introduction • Artificial Neural Networks may be considered as much more powerful because it can solve problems where how to solve have been not known exactly. • Uses of artificial neural network have been spread to a wide range of domain like image recognition, fingerprint recognition and so on.
Related work(1/4) • The appearance of digital computers and the development of modern theories of learning and neural processing both occurred at about the same time, during the late 1940s. • To model individual neurons as well as clusters of neurons, which are called neural networks.
Related work(2/4) • A new approach for feature extraction based on the calculation of eigen values from a contour was proposed and found that using feed forward neural network satisfactory results were obtained.
Related work(3/4) • Feed Forward Neural Network
Method • Processing of Original Image • The initial optimal image has been taken as furnished in Fig -2 which has been considered as original image. Fig-2 Table-1 Input Data Matrix
Method • The average error after insertion of salt and pepper noise has been calculated which is 25.67%. Fig-3 Table-2 Input Data Matrix with Noise
Method • Processing of Noisy Image • Adaptive median Filter has been applied on noisy image such that the noise can be removed and the output image would be considered as filtered Image. • The estimated Error and average error of the values stored in filtered image matrix have been calculated with reference to the values stored in original data matrix. The average error has been found as 5.397%.
Method • The original image after removal of noise has been transformed into data matrix containing pixel values which have been furnished in Table -3. Fig-4 Table-3 Input Data Matrix after Noise Removal
Method • For easier calculation four pixels have been taken together. • The binary values of four pixels together side by side have been combined and formed as 32 bit binary number. • Now the 32 bit binary number has been converted into a decimal number.
Method • The decimal number as generated in page 11has been placed in original data matrix termed as ORMAT[][]. Table-4 Original Data Matrix ORMAT[][]
Method • The instructions furnished in page 12 to page 13 have been repeated for the total pixel value of the original image after noise removal as stored in Table -3. • Therefore a matrix has been produced which has been stored in data matrix termed as ORMAT[][] as furnished in Table-4.
Method • Processing of second Image(Test Image) • A new image has been taken which is considered as a test image. • Now it is necessary to check whether the said image can be recognized or not. Fig-5 Table-5 Test Data Matrix
Method • Instructions as furnished in page 9have been executed on test image to generate test data matrix with noise as furnished in Table -6. Fig-6 Table-6 Test Data Matrix with Noise
Method • Instructions as furnished in page 10 have been executed on test image with noise to generate test data matrix after noise removal as furnished in Table -7. Fig-7 Table-7 Test Data Matrix after Noise Removal
Method • Procedures as mentioned from page 11 to page 13 have been executed on test image after noise removal to generate the decimal number which has been placed in test data matrix TESTMAT[][]. Table-8 TESTMAT[][]
Method • Calculation of Average Error of test data matrix based on original data matrix. • The estimated error and average error of the values stored in decimal matrix as furnished in Table-9 have been calculated with reference to the values stored in original data matrix as stored in Table -4. The average error has been found as 31%.
Method • Since the average error is less than 45%, necessary steps regarding the processing of test image has been made using the technique of artificial neural network for the purpose of recognition. Table-9 Estimated Error Data
Method • Processing of Image towards recognition using Artificial Neural Network. • The feed forward back propagation neural network has been used on the test data matrix of the test image for training and testing with reference to the original data matrix of the original image. • A new data matrix named NEWMAT[][] has been produced as a result which has been furnished in Table -10.
Method • It takes considerably less time to complete the training and Testing using ANN. Table-10 Data Matrix NEWMAT[][] after ANN application
Method • Each value of the data matrix NEWMAT[][] has been converted into 32 bit binary number. • Now the 32 bit binary number has been divided into four 8 bit binary numbers. • Each 8 bit binary value has been converted into decimal and each of them has been considered as pixel values for four consecutive pixels row wise.
Method • The instructions furnished in page 23 have been repeated for the total values of the data matrix NEWMAT[][]. • As a result a new modified data Matrix named MODMAT[][] has been produced as furnished in Table -11.
Method Fig-8 Table-11 Modified Data Matrix MODMAT[][]
Method • Calculation of estimated Error and Average Error. • The estimated error and average error of the values as stored in Table -11 with reference to the values stored in Table -3 have been calculated and the average error has been found as 14.39%.
Conclusion • If the average error is less than 45%, Artificial Neural network can be applied for training and testing for the purpose of recognition. • Therefore the test image is recognized and matched successfully with original image.
Conclusion • If the average error is greater than 45% then the image is recognized as a different image. • It takes less time for training and testing using ANN as number of rows of the matrix used for training has one fourth numberof columns compare to the original image.
